Spending on Social Welfare Programs in Rich and Poor States

Chapter II:

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  1. Overview
  2. Fifty-State Analysis
    1. Overview of Analysis
    2. Overview of Data Sources and How the Data Were Used
  3. Site Visits
    1. Purpose
    2. Approach
    3. Selection of Poor States
  4. Integrated Analysis Between Econometric Model And Site Visit Data

A. Overview

To answer the research questions identified in the Introduction, first we analyzed spending trends and patterns over 24 years for the 50 states plus the District of Columbia by using sample means from Census data and an econometric model estimated from the pooled time series and cross-section data. We supplemented this analysis with site visits and further analysis of qualitative and quantitative data from six states, selected as having high needs relative to their fiscal capacity. The econometric model estimates were used to identify states exhibiting a high propensity to spend on certain types of social welfare. Employing this information, we drew comparisons between rich states and poor states in general and also among the six states for which we had additional case study data.

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B. Fifty-State Analysis

1. Overview of Analysis

Our analysis of Census spending data involved both examining time trends and patterns across states and estimating an econometric model. The analysis of trends and patterns entailed primarily the study of time trends and cross-state patterns for different components of spending. We developed the econometric model to give insight into the determinants of state and local spending on social welfare and to provide useful input for the analysis of spending decisions between rich and poor states in general and in the six poor states we visited. As explained below, the model specification is based on a linear expenditure function derived from a behavioral theory of state and local spending decisions subject to a budget constraint. The model attempts to identify causal factors that explain variation in spending patterns. The explanatory factors include state fiscal capacity, federal grant amounts, indicators of need for social welfare programs (e.g., poverty and unemployment), time effects (e.g., dummy variables for year), and state effects (e.g., dummy variables for state). We did not intend the model to capture all factors responsible for variation in spending. Indeed, to help us understand variation in spending across states, we relied on measures of "state effects," which are effects associated with particular states after controlling for the effects of included explanatory variables.

a. Analysis of Trends and Patterns

Our analysis of time trends and cross-state patterns in social welfare spending involved examining Census data on per capita spending by state and local governments for the period 1977 to 2000 for each of the 50 states plus the District of Columbia. In subsection IIC below, we describe in detail the data examined. A central focus of the trends and patterns analysis was to identify important differences in real state and local spending by category of social welfare (e.g., cash assistance, Medicare, and non-health social welfare) and non-social welfare spending and by states grouped into quartiles based on average per capita personal income. This essentially descriptive analysis of the data served as a prelude to developing a model to identify causal factors related to state and local spending on social welfare and non-social welfare functions.

b. Linear Expenditure Models

Following McGuire (1978) for the basic theoretical model, we assumed that the decision-maker is the combined state and local system. This approach allowed us to model state and local spending on both public and private goods in a consistent way. The budget constraint consists of state fiscal capacity augmented by federal grants. The state and local governments then make spending and taxation choices subject to this budget constraint. States that have preferences for public spending over private consumption tend to raise more from their citizens in taxes and allocate such tax revenue to public spending. The theoretical foundation for the linear expenditure model used in this report is described more fully in McGuire (1978) and Appendix A to this report.

i) Dependent Variable: Spending Per Capita

The dependent variable in the regression was total state and local spending per capita on public functions defined for five general areas: (1) cash assistance, (2) Medicaid,(7) (3) other non-health social services (e.g.,foster care, child care, low-income energy assistance), (4) spending on public hospitals(8); and (5) non-social welfare (e.g., education, transportation, law enforcement).(9) The total state and local spending for particular categories includes the federal grants that the state and localities spend. However, such federal grants are not reported in the Census of Governments at the same level of detail as is the spending activity. Federal grants are reported only for social welfare spending as a whole, which fails to include public hospitals, and for non-social welfare spending. This practice makes measuring spending impossible from state and local sources at the higher levels of disaggregation for which we report types of social welfare spending. The price deflators used in the analysis depended on the type of spending. For cash assistance and other non-health social welfare spending, we used the general gross domestic product (GDP) price deflator. For Medicaid and public hospitals, we used the Consumer Price Index (CPI) for health care, which we believe better captures price trends in the health sector than does the overall deflator.

ii) Explanatory Variables

The explanatory, or independent, variables in the regression models included a measure of fiscal capacity, measures of the need for social welfare spending, state effect dummy variables, and year dummy variables. In addition, some but not all models attempted to capture price effects of federal grants using a McGuire-type analysis (see Appendix A).

Fiscal Capacity

We considered the possibility of creating a consistent data series incorporating some or all of the elements of the PCPI, RTS, and TTR approaches, as discussed in the Introduction. However, the PCPI model is the easiest and most reliable to implement because consistent data are available across states from the Bureau of Economic Analysis (BEA). Therefore, in our models, state and local resources (i.e., fiscal capacity) are measured by state per capita personal income, deflated by the implicit price deflators of the general GDP relative to 1996.

Federal Grants

Federal grant amounts for social welfare and non-social welfare functions as measured by the Census data on intergovernmental revenues appear as explanatory variables in the model. Because both variables enter the "budget constraint" for the public decision-makers, the effects of federal grants might be thought comparable to the effects of personal income. However, research on public expenditures has identified what has been termed the "flypaper effect" (i.e., money sticks where it hits) through which federal grant money exerts a greater stimulatory effect on public spending than increases in private income on public spending (Gramlich, 1977; Hines & Thaler, 1995; Gamkhar & Oates, 1996). We accommodated the likelihood of a flypaper effect by introducing grants into the model as explanatory variables separately from our measure of state fiscal capacity. We also adjusted federal grants for inflation using the general GDP price deflator.

Need Variables

As measures of the (relative) need for spending on social welfare in the core model, we included:

These variables are thought to capture need for social welfare spending for several reasons. First, families in poverty are more likely to qualify for cash assistance and also need social services. Second, unemployment captures economic downturns and also economic hardship associated with involuntary unemployment. Third, population density is a variable that has in other studies been correlated with government spending. It undoubtedly captures a number of effects, including urbanization and special resource costs associated with a high population density.

A number of additional factors might influence state spending. We have chosen the three variables identified above because poverty, unemployment, and population density are thought to proxy in slightly different ways for the need for social welfare spending. We considered adding poverty for various subgroups, such as children and elderly, but the data were unavailable over a sufficient time period. We also experimented with per capita number of persons in urban areas, but this variable was highly correlated with population density, and population density seemed more strongly associated with per capita spending than urbanization.

State Effect Variables

The state effect variables, as described above, were dummy variables defined as one for a particular state and zero otherwise. Inclusion of such variables in the regression allowed us to estimate separate intercepts for each state.(10) These state effects were useful in assessing how much of the spending on certain categories of social welfare was due to explanatory variables, such as per capita personal income, per capita federal grants, or need variables, and how much was due to an inherent willingness of the state to spend on the particular function. The state effect variables were important in our analysis of spending patterns in the six states selected for more in-depth study and in comparing spending among rich and poor states in general.

Time Trends

We captured time trends in the models primarily by dummy variables for year. The magnitude of the coefficients on the year dummies indicated whether spending increased or decreased following certain seminal policy initiatives or shifts, such as welfare reform or expansion in Medicaid eligibility. We did not interact the year dummies with the state dummies in general because we had no specific hypotheses to test state by state. However, we estimated the quartile regressions with and without the year dummies, essentially estimating different time trends for rich and poor states.

c. Cyclical Models

An important question is how state and local spending responds to changes in the level of overall economic activity such as booms and recessions and changes in state labor market conditions. To some extent, the unemployment variable in the need analysis incorporates the effects of unemployment on state and local spending. In addition, we developed other models that examined the effect of the state unemployment rate alone and in conjunction with other variables, such as per capita personal income, on per capita spending. We expected that any negative effect on spending of increased state unemployment would occur at least in significant part through the reduction in state per capita personal income that occurs when unemployment rises. Our models allowed us to test this hypothesis.

2. Overview of Data Sources and How the Data Were Used

We considered several data sources on state spending for estimating the 50-state econometric model, including data from the U.S. Census Bureau (Census Bureau), the National Association of State Budget Officers (NASBO), the National Conference of State Legislatures (NCSL), and federal departments. No data source provides comprehensive, detailed measures of social services spending comparable across states and time. In addition, other sources are available for a limited number of states or covering limited periods of time. We relied primarily on the Census of Governments for data on spending by states and localities and estimated federal grants through intergovernmental revenues. Below, we summarize the primary data sources used in our analysis.

The Census Bureau collects finance data from state and local governments and aggregates these data for each state and year at the state level, providing information on revenue and expenditures of state government, revenue and expenditures for all local governments in aggregate within a state, and for state and local governments combined. The state and local expenditure data include state and local spending with federal grants. One of the largest and broadest expenditure categories, public welfare expenditure, amounted to $233 billion in state and local expenditures in fiscal year 2000.(11)

To measure social welfare spending, we aggregated several Census data categories. Exhibit II-1 breaks 2000 public welfare spending, in billions of dollars, into its detailed components, using the Census Bureau's codes and also shows how we aggregated the Census categories.

Exhibit II-1
State and Local Government Spending by Detailed Item, FY 2000
(in billions)
Spending Category Detailed Item in Census Data FY 2000 $
Cash Assistance E67
Federal Categorical Assistance
17.7 Includes AFDC or TANF cash assistance; federal Supplemental Security Income (SSI) when it passes through state accounts; and state SSI supplements
Note: The only federal SSI funds that pass through state accounts are retroactive federal payments reimbursing the state for payments made to individuals under state supplement programs; the total amounts are small
Other Cash Assistance
3.0 Includes cash assistance programs not under federal categorical programs (e.g., general assistance, refugee assistance, home relief, and emergency relief)
Medicaid E74
Vendor Payments for Medical Care
155.0 Includes payments made directly to private vendors for medical assistance and hospital and health care (payments consist mostly of Medicaid/SCHIP)
Non-health Social Services E75
Vendor Payments for Other Purposes
2.1 Includes payments made directly to private vendors for services and commodities other than medical, hospital, and health care
E77, F77, G77 Welfare Institutions 1.2 Includes payments for provision, construction, and maintenance of nursing homes and welfare institutions owned and operated by a government for the benefit of needy persons
E79, F79, G79 Other Public Welfare 54.4 Includes operational payments for public employees in the sphere of public welfare; and payments for welfare programs such as child care, child welfare, adoption assistance, foster care, low-income energy assistance and weatherization, social services to the physically disabled, SSBG-funded programs, welfare-related community action programs, and temporary shelters and other services for the homeless
Public Hospitals E36, F36, G36 Own Hospitals (except federal veterans) 75.2 Includes payments for hospital facilities providing in-patient medical care and institutions primarily for the care and treatment of the disabled that are directly administered by a government as well as direct payments for acquisition or construction of hospitals
Non-social Welfare All Other Spending 1,309.0 Includes primarily elementary and secondary education, higher education, highways and public mass transit, police protection, financial administration, housing and community development, utilities (water supply and electric power), and sewerage

As shown in Exhibit II-1, there are six major categories of social welfare spending as defined in the Census as well as Own Hospitals, or state-run hospitals, which the Census does not count as part of social welfare spending. To estimate the model, we combined several categories of data. Federal Categorical Assistance and Other Cash Assistance were combined into a single Cash Assistance variable.(12) Vendor Payments for Medical Care (chiefly Medicaid) served as its own category.(13) The remaining three Census categories (Vendor Payments for Other Purposes, Welfare Institutions, and Other Public Welfare) were combined into a single Non-health Social Services category.(14) Additionally, we created two more categories of spending from Census data, one for Public Hospitals shown in Exhibit II-1 as Own Hospitals and a residual category of all other spending that we called Non-social Welfare.

Per capita personal income was taken from BEA data and adjusted for inflation using the price deflator from the National Income Accounts. We measured the need variables by Bureau of Labor Statistics data (unemployment) and Bureau of Census data (poverty and population density).

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C. Site Visits

1. Purpose

The study included site visits to six states for in-depth answers to questions about how state fiscal capacity affects state spending on social programs. We visited states with low fiscal capacity and high social needs to understand how such states coped with this seeming imbalance. Three questions were of particular interest:

Thus, we devised a research plan to (1) discern variation in spending levels and program emphases among poor states, (2) identify the processes of decision-making that affected spending on social programs as well as influences on those processes, (3) use this information to assess the credibility of hypotheses about differences in spending by poor states, and (4) examine how state decisions were affected by economic expansions and contractions, such as the recent downturn in state revenues in FYs 2001 and 2002.

2. Approach

The site visits consisted of discussions with state officials in each of six states about state budget processes and choices involving major social programs, including TANF cash and non-cash assistance, child care subsidies, child welfare, Medicaid, SCHIP, SSBG, and others. We met with high-level agency administrators responsible for these programs, legislative aides involved in the budget process, executive budget officials, and gubernatorial staff.

Though the conversations were relatively unstructured, we began the site visits with a list of topics to be addressed:

To help answer these questions, we also collected budget documents and other materials describing the state's social programs, the agencies administering them, and budget procedures and rules. Finally, to help trace changes in state spending and make comparisons across states, we compiled and analyzed administrative data on major state programs, such as TANF, Medicaid, and the Child Care and Development Fund (CCDF).

3. Selection of Poor States

Selected for site visits through a three-step process, the six states were Arizona, Louisiana, Mississippi, New Mexico, South Carolina, and West Virginia. First, we ranked all states by an index composed of state fiscal incapacity (i.e., per capita personal income, inversely scored) and social needs (i.e., federal poverty rates) and eliminated the two lower quartiles. Second, we scored the states on several other indicators of need and resources (e.g., children without health insurance, unemployment rates, and alternative measures of fiscal incapacity) and eliminated states that showed several discrepancies (i.e., indicators of wealth or low needs). Third, we reviewed the remaining 12 states to ensure that they fit the basic criteria while still offering useful variation for comparisons. When selected, the six states seemed to divide into three main groupings:

These states offered sufficient variation in spending levels, program emphases, political cultures, budget processes, constituencies, and other factors to give us leverage in generating plausible hypotheses about influences (i.e., how the effects of fiscal capacity might play out under different governmental, economic, and cultural conditions).

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D. Integrated Analysis Between Econometric Model and Site Visit Data

We designed our field research in part to help interpret the estimated econometric models and extend the range of factors whose influences might be assessed. At the most general level, the analyses of the econometric data posed and sharpened the questions for the site visits and related analyses of the six states.

First, the site visits were used to weigh the credibility of different explanations of the estimated regression coefficients. For example, the econometric models found that population density exerted positive effects on cash assistance expenditures and negative effects on health-related spending. The site visits suggested hypotheses, consistent with a wide array of quantitative and qualitative data, as to why these differences might occur, at least in poor states.

Second, the econometric models estimated state effects for total as well as different types of social welfare spending. Intercepts estimated for each state,(15) these coefficients represented an average level of spending for a particular state after controlling for the effects of all included variables, such as fiscal capacity, unemployment, and poverty. Because these state-effect estimates stripped off the linear effects of economic and demographic variables, they varied greatly among the six poor states and helped sharpen our analyses of institutional and political factors. For example, though Mississippi's spending on medical assistance could not be considered high in an absolute sense, it was sizeable after controlling for the state's fiscal capacity and other significant variables. Thus, the econometric analysis changed the question from why the state's spending on medical assistance was so low to why it was higher than we had expected, given the state's economy and demographics.

Third, the estimated state effects allowed us to examine with greater precision how states varied in the ways they combined, or failed to combine, different types of social welfare expenditures. For example, we found a fundamental division between poor states (i.e., between states that put enormous emphasis on medical assistance and other states whose long-run spending tendencies were more balanced between different functions). We estimated these different configurations, or packages, of spending through the econometric analysis and posed important questions for the site visits.

Fourth, the six state case studies allowed us to assess findings from the econometric analyses in light of state spending changes after fiscal year 2000, the last year for which Census Bureau spending data were available. For example, the models indicated that spending on cash assistance and Medicaid went up during recessions and down during economic booms, other things being equal, while non-health social services showed the opposite pattern. Because the states we studied were, for the most part, experiencing severe fiscal pressures after several years of economic growth, we could draw on quantitative and qualitative data in the case studies to tests these and other expectations.

We also estimated separate econometric models for each of the six states in the field research sample, and we thought these separate estimates would clarify other important differences and similarities among these states. However, with few exceptions, these separate models also turned out to be hard to interpret because of instability, we suspect, due to small degrees of freedom. Thus, we do not present these models in the current report.

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(7) The Census defines this category, which is primarily Medicaid spending, as "payments to medical vendors."

(8) Census does not consider spending on state run hospitals social welfare spending because the patients at such hospitals might not be predominantly low-income. However, some part of the federal grants, which we measured by Census intergovernmental revenues, goes to support public hospitals primarily through the Medicare and Medicaid Disproportionate Share Hospital (DSH) program. Therefore, examining how spending on public hospitals varied across states was important. Unfortunately, Census considers grants for public hospitals to be grants for non-social welfare and fails to disaggregate grant amounts by detailed function. So, we were unable to identify federal grants for public hospitals and thus were unable to separate public hospital spending into a federal share component and a state and local share component as we could do for overall Census social welfare spending. Nonetheless, we used spending per capita on public hospitals as a dependent variable in most of our regression analyses where we did not have to identify separately the federal and state and local funding components.

(9) Definitions for these five categories in the Census data used in our analysis appear in Exhibit II-1.

(10) The coefficients on the explanatory variables were not allowed to vary across states in our general model, but we did estimate the regression separately for each quartile defined in terms of average per capita personal income. These quartile regressions estimated the coefficients separately for each quartile. However, the estimated state effects used in our cross-state analysis (see subsection III .B.4.) came from the regression estimated over all states. These state effects captured differences in state spending unexplained by the variables in the fixed coefficient model. Some part of these effects could be due to the fact that states had different responses (i.e., variable coefficients) to the explanatory variables.

(11) Public welfare expenditure includes all of the categories shown in Exhibit II-1, except Public Hospitals. The Census views spending on state-run public hospitals as outside its social welfare category. However, we included spending on public hospitals as a variable of interest, partly because state-run public hospitals receive Medicaid funding and also because low-income individuals might receive services in the public hospitals.

(12) The Federal Categorical Assistance category (E67) tracks federally funded programs and includes AFDC cash assistance, TANF cash assistance, or both, to the extent it passes through state accounts; federal Supplemental Security Income (SSI); plus state supplements. The only federal SSI included in E67 is retroactive federal payments to reimburse the state for payments made to individuals under state supplement programs. The Other Cash Assistance Programs category (E68) includes cash assistance programs not under federal categorical programs.

(13) As noted, Vendor Payments for Medical Care is the largest category by far and consists mostly of Medicaid.

(14) The Other Public Welfare category (E79) includes operational payments for administrative workers and payments for programs such as child care, foster care, low-income energy assistance, social services to the physically disabled, and programs funded by the Social Services Block Grant.

(15) The "state effect" for each state was computed by adding the intercept to the coefficient of the dummy variable for the state.

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